![Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness](https://c.mql5.com/2/84/Creating_an_Interactive_Graphical_User_Interface_in_MQL5_Part_2_600x314.jpg)
Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness
Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.
![Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram](https://c.mql5.com/2/88/Creating_an_MQL5-Telegram_Integrated_Expert_Advisor_tPart_1w_600x314.jpg)
Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram
In this article, we create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.
![Practicing the development of trading strategies](https://c.mql5.com/2/73/Experience_in_developing_a_trading_strategy_600x314.jpg)
Practicing the development of trading strategies
In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.
![Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning](https://c.mql5.com/2/59/NN_easy_61_SPLT_V2__600x314.jpg)
Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning
During the offline learning, we optimize the Agent's policy based on the training sample data. The resulting strategy gives the Agent confidence in its actions. However, such optimism is not always justified and can cause increased risks during the model operation. Today we will look at one of the methods to reduce these risks.
![MQL5 Wizard Techniques you should know (Part 07): Dendrograms](https://c.mql5.com/2/59/Dendrograms_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 07): Dendrograms
Data classification for purposes of analysis and forecasting is a very diverse arena within machine learning and it features a large number of approaches and methods. This piece looks at one such approach, namely Agglomerative Hierarchical Classification.
![Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)](https://c.mql5.com/2/84/Introduction_to_MQL5_Part_8_Beginners_Guide_to_Building_Expert_Advisors_600x314.jpg)
Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)
This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.
![Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group](https://c.mql5.com/2/74/Developing_a_multi-currency_advisor_Part_6_Automating_the_selection_of_a_group_of_instances_600x314.jpg)
Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group
After optimizing the trading strategy, we receive sets of parameters. We can use them to create several instances of trading strategies combined in one EA. Previously, we did this manually. Here we will try to automate this process.
![Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)](https://c.mql5.com/2/82/Building_A_Candlestick_Trend_Constraint_Model_Part_5_Next-7iSmtcwWt-transformed_600x314.jpg)
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)
Today, we are discussing a working Telegram integration for MetaTrader 5 Indicator notifications using the power of MQL5, in partnership with Python and the Telegram Bot API. We will explain everything in detail so that no one misses any point. By the end of this project, you will have gained valuable insights to apply in your projects.
![Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR](https://c.mql5.com/2/89/midjourney_image_15570_399_3853_600x314.jpg)
Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR
In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.
![Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)](https://c.mql5.com/2/58/replay-p28_600x314.jpg)
Developing a Replay System (Part 29): Expert Advisor project — C_Mouse class (III)
After improving the C_Mouse class, we can focus on creating a class designed to create a completely new framework fr our analysis. We will not use inheritance or polymorphism to create this new class. Instead, we will change, or better said, add new objects to the price line. That's what we will do in this article. In the next one, we will look at how to change the analysis. All this will be done without changing the code of the C_Mouse class. Well, actually, it would be easier to achieve this using inheritance or polymorphism. However, there are other methods to achieve the same result.
![Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group](https://c.mql5.com/2/74/Developing_a_multi-currency_advisor_Part_6_Automating_the_selection_of_a_group_of_instances_600x314.jpg)
Developing a multi-currency Expert Advisor (Part 6): Automating the selection of an instance group
After optimizing the trading strategy, we receive sets of parameters. We can use them to create several instances of trading strategies combined in one EA. Previously, we did this manually. Here we will try to automate this process.
![Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer](https://c.mql5.com/2/69/Neural_networks_made_easy_qPart_767_Exploring_various_modes_of_interaction_Multi-future_Transformer_.jpg)
Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer
This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.
![Category Theory in MQL5 (Part 17): Functors and Monoids](https://c.mql5.com/2/57/Category-Theory-p17_600x314.jpg)
Category Theory in MQL5 (Part 17): Functors and Monoids
This article, the final in our series to tackle functors as a subject, revisits monoids as a category. Monoids which we have already introduced in these series are used here to aid in position sizing, together with multi-layer perceptrons.
![MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent](https://c.mql5.com/2/83/MQL5_Wizard_Techniques_you_should_know_Part_26_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 26): Moving Averages and the Hurst Exponent
The Hurst Exponent is a measure of how much a time series auto-correlates over the long term. It is understood to be capturing the long-term properties of a time series and therefore carries some weight in time series analysis even outside of economic/ financial time series. We however, focus on its potential benefit to traders by examining how this metric could be paired with moving averages to build a potentially robust signal.
![Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU](https://c.mql5.com/2/82/Integrate_Your_Own_LLM_into_EA_Part_4_600x314.jpg)
Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5](https://c.mql5.com/2/86/Creating_a_Dynamic_Multi-Symbol_Indicator_Dashboard_in_MQL5_600x314.jpg)
Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5
In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.
![Neural networks are easy (Part 59): Dichotomy of Control (DoC)](https://c.mql5.com/2/59/Caregory_600x314.jpg)
Neural networks are easy (Part 59): Dichotomy of Control (DoC)
In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.
![Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)](https://c.mql5.com/2/82/Data_Science_and_ML_Part_25_600x314.jpg)
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
![Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram](https://c.mql5.com/2/88/midjourney_image_15495_381_3675_1_600x314.jpg)
Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram
In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.
![Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)](https://c.mql5.com/2/82/Data_Science_and_ML_Part_25_600x314.jpg)
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
![MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial](https://c.mql5.com/2/70/MQL5_Wizard_Techniques_you_should_know_Part_12_Newton_Polynomial_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial
Newton’s polynomial, which creates quadratic equations from a set of a few points, is an archaic but interesting approach at looking at a time series. In this article we try to explore what aspects could be of use to traders from this approach as well as address its limitations.
![Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5](https://c.mql5.com/2/73/Introduction_to_MQL5_Part_5_600x314.jpg)
Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5
Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!
![Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)](https://c.mql5.com/2/73/Neural_networks_are_easy_Part_81_600x314.jpg)
Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)
In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.
![Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks](https://c.mql5.com/2/84/Data_Science_and_ML_Part_26_600x314.jpg)
Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks
In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.
![MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function](https://c.mql5.com/2/88/MQL5_Wizard_Techniques_you_should_know_Part_31_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function
Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.
![Propensity score in causal inference](https://c.mql5.com/2/72/Propensity_score_in_causal_inference___600x314.jpg)
Propensity score in causal inference
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
![Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners](https://c.mql5.com/2/85/Combine_Fundamental_And_Technical_Analysis_Strategies_in_MQL5_For_Beginners_600x314.jpg)
Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners
In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.
![Data label for time series mining (Part 6):Apply and Test in EA Using ONNX](https://c.mql5.com/2/64/Data_label_for_time_series_mining_wPart_6v_Apply_and_Test_in_EA_Using_ONNX_600x314.jpg)
Data label for time series mining (Part 6):Apply and Test in EA Using ONNX
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state](https://c.mql5.com/2/71/Neural_networks_are_easy_Part_79_600x314.jpg)
Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state
In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.
![Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU](https://c.mql5.com/2/79/Integrate_Your_Own_LLM_into_EA__Part_3_-_Training_Your_Own_LLM_with_CPU_600x314.jpg)
Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Combinatorially Symmetric Cross Validation In MQL5](https://c.mql5.com/2/60/Combinatorially_Symmetric_Cross_Validation_600x314.jpg)
Combinatorially Symmetric Cross Validation In MQL5
In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.
![Reimagining Classic Strategies: Crude Oil](https://c.mql5.com/2/79/Reimagining_Classic_Strategies____Crude_Oil_600x314.jpg)
Reimagining Classic Strategies: Crude Oil
In this article, we revisit a classic crude oil trading strategy with the aim of enhancing it by leveraging supervised machine learning algorithms. We will construct a least-squares model to predict future Brent crude oil prices based on the spread between Brent and WTI crude oil prices. Our goal is to identify a leading indicator of future changes in Brent prices.
![Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)](https://c.mql5.com/2/69/Population_optimization_algorithms___Artificial_Multi-Social_Search_Objects_zMSO4_600x314.jpg)
Population optimization algorithms: Artificial Multi-Social Search Objects (MSO)
This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.
![From Novice to Expert: The Essential Journey Through MQL5 Trading](https://c.mql5.com/2/86/MQL5_Mastery_Companion_600x314__1.jpg)
From Novice to Expert: The Essential Journey Through MQL5 Trading
Unlock your potential! You're surrounded by opportunities. Discover 3 top secrets to kickstart your MQL5 journey or take it to the next level. Let's dive into discussion of tips and tricks for beginners and pros alike.
![MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial](https://c.mql5.com/2/75/MQL5_Wizard_Techniques_You_Should_Know_wPart_15y_600x314.jpg)
MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial
Support Vector Machines classify data based on predefined classes by exploring the effects of increasing its dimensionality. It is a supervised learning method that is fairly complex given its potential to deal with multi-dimensioned data. For this article we consider how it’s very basic implementation of 2-dimensioned data can be done more efficiently with Newton’s Polynomial when classifying price-action.
![MQL5 Wizard Techniques you should know (Part 32): Regularization](https://c.mql5.com/2/90/image_15576_600x314.jpg)
MQL5 Wizard Techniques you should know (Part 32): Regularization
Regularization is a form of penalizing the loss function in proportion to the discrete weighting applied throughout the various layers of a neural network. We look at the significance, for some of the various regularization forms, this can have in test runs with a wizard assembled Expert Advisor.
![Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)](https://c.mql5.com/2/58/replay-p30_600x314.jpg)
Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)
Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.
![Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)](https://c.mql5.com/2/60/Neural_networks_are_easy_aPart_63n_600x314.jpg)
Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)
We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.
![Data label for time series mining (Part 5):Apply and Test in EA Using Socket](https://c.mql5.com/2/64/Data_label_for_time_series_miningcPart_5c_Apply_and_Test_in_EA_Using_Socket_600x314.jpg)
Data label for time series mining (Part 5):Apply and Test in EA Using Socket
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library](https://c.mql5.com/2/80/MQL5_Trading_Toolkit_Part_1_600x314.jpg)
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.